Main Analysis
Jake Hughey
2022-08-17
Last updated: 2022-08-27
Checks: 6 1
Knit directory: schoolsout/
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Load packages and data.
library('bayesplot')
library('rstanarm')
# library('broom')
library('cowplot')
library('data.table')
library('foreach')
library('ggplot2')
library('haven')
# library('huxtable')
library('kableExtra')
library('knitr')
# theme_set(
# theme_bw() +
# theme(axis.text = element_text(color = 'black'),
# panel.grid.minor = element_blank(),
# legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = 'cm')))
dataDir = 'data'
dOrig = setDT(read_dta(file.path(dataDir, 'master.dta')))
treat_types = c('treat_pool', 'treat_target')Melt data.table to long format, scale outcomes by standard deviation of the control group, and rename stuff.
outcomes = data.table(
level = c('average_level', 'place_value_correct', 'operation_frac_correct'),
label = c('Average level', 'Place value', 'Fractions'))
dMelt = melt(
dOrig,
id.vars = c('unique_id', 'treatment', 'treat_pool', 'treat_target', 'tarl_prev'),
measure.vars = outcomes$level, variable.name = 'outcome_name',
value.name = 'outcome_value', variable.factor = FALSE)
dMelt[, outcome_value := as.numeric(outcome_value)]
dMelt[
, outcome_value := outcome_value / sd(outcome_value[treatment == 0], na.rm = TRUE),
by = outcome_name]
dMelt[, outcome_name := factor(outcome_name, outcomes$level, outcomes$label)]
for (j in treat_types) {
a = attr(dOrig[[j]], 'labels')
dMelt[, x := factor(x, a, names(a)), env = list(x = j)]}Fit Bayesian models
dFit = foreach(treat_type = treat_types, .combine = rbind) %do% {
dMelt[
, .(treat_type = treat_type,
fit_sep = list(stan_glm(
outcome_value ~ x + tarl_prev, data = .SD, refresh = 0)),
fit_agg = list(stan_glm(
outcome_value ~ I(x != 'Control') + tarl_prev, data = .SD, refresh = 0))),
keyby = outcome_name, env = list(x = treat_type)]}
dFit[, draws := list(list(as.array(fit_sep[[1L]]))), by = 1:nrow(dFit)]Plot densities of posterior draws
pList = foreach(i = 1:nrow(dFit)) %do% {
p = mcmc_areas(dFit[i]$draws[[1L]], regex_pars = '^treat', prob = 0.95)
p = p + scale_y_discrete(labels = function(x) sub('^treat_(pool|target)', '', x))
if (i <= 3) p = p + ggtitle(dFit$outcome_name[i])
p}
p = plot_grid(plotlist = pList, nrow = 2L, align = 'hv')
p
Plot uncertainty intervals of posterior draws
pList = foreach(i = 1:nrow(dFit)) %do% {
p = mcmc_intervals(dFit[i]$draws[[1L]], regex_pars = '^treat', prob_outer = 0.95)
p = p + scale_y_discrete(labels = function(x) sub('^treat_(pool|target)', '', x))
if (i <= 3) p = p + ggtitle(dFit$outcome_name[i])
p}
p = plot_grid(plotlist = pList, nrow = 2L, align = 'hv')
p
Compute 90% posterior intervals
dInt = dFit[
, data.table(posterior_interval(fit_sep[[1L]], prob = 0.9, regex_pars = '^treat')),
keyby = .(outcome_name, treat_type)]
setnames(dInt, 3:4, c('ci_low', 'ci_high'))
kable_paper(kbl(dInt, digits = 3), 'hover', full_width = FALSE)| outcome_name | treat_type | ci_low | ci_high |
|---|---|---|---|
| Average level | treat_pool | -0.050 | 0.097 |
| Average level | treat_pool | 0.046 | 0.195 |
| Average level | treat_target | -0.003 | 0.146 |
| Average level | treat_target | -0.003 | 0.149 |
| Place value | treat_pool | -0.064 | 0.084 |
| Place value | treat_pool | 0.041 | 0.189 |
| Place value | treat_target | -0.048 | 0.100 |
| Place value | treat_target | 0.025 | 0.168 |
| Fractions | treat_pool | -0.025 | 0.121 |
| Fractions | treat_pool | 0.002 | 0.151 |
| Fractions | treat_target | -0.044 | 0.106 |
| Fractions | treat_target | 0.019 | 0.168 |
sessionInfo()R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] knitr_1.40 kableExtra_1.3.4 haven_2.5.1 ggplot2_3.3.6
[5] foreach_1.5.2 data.table_1.14.3 cowplot_1.1.1 rstanarm_2.21.3
[9] Rcpp_1.0.9 bayesplot_1.9.0
loaded via a namespace (and not attached):
[1] minqa_1.2.4 colorspace_2.0-3 ellipsis_0.3.2
[4] ggridges_0.5.3 rprojroot_2.0.3 markdown_1.1
[7] base64enc_0.1-3 fs_1.5.2 rstudioapi_0.14
[10] farver_2.1.1 rstan_2.21.5 DT_0.24
[13] fansi_1.0.3 xml2_1.3.3 codetools_0.2-18
[16] splines_4.2.1 cachem_1.0.6 shinythemes_1.2.0
[19] jsonlite_1.8.0 workflowr_1.7.0 nloptr_2.0.3
[22] shiny_1.7.2 readr_2.1.2 compiler_4.2.1
[25] httr_1.4.4 backports_1.4.1 assertthat_0.2.1
[28] Matrix_1.4-1 fastmap_1.1.0 cli_3.3.0
[31] later_1.3.0 htmltools_0.5.3 prettyunits_1.1.1
[34] tools_4.2.1 igraph_1.3.4 gtable_0.3.0
[37] glue_1.6.2 posterior_1.3.0 reshape2_1.4.4
[40] dplyr_1.0.9 jquerylib_0.1.4 vctrs_0.4.1
[43] svglite_2.1.0 nlme_3.1-159 iterators_1.0.14
[46] crosstalk_1.2.0 tensorA_0.36.2 xfun_0.32
[49] stringr_1.4.1 ps_1.7.1 lme4_1.1-30
[52] rvest_1.0.3 mime_0.12 miniUI_0.1.1.1
[55] lifecycle_1.0.1 gtools_3.9.3 MASS_7.3-58.1
[58] zoo_1.8-10 scales_1.2.1 colourpicker_1.1.1
[61] hms_1.1.2 promises_1.2.0.1 parallel_4.2.1
[64] inline_0.3.19 shinystan_2.6.0 yaml_2.3.5
[67] gridExtra_2.3 loo_2.5.1 StanHeaders_2.21.0-7
[70] sass_0.4.2 stringi_1.7.8 highr_0.9
[73] dygraphs_1.1.1.6 checkmate_2.1.0 boot_1.3-28
[76] pkgbuild_1.3.1 systemfonts_1.0.4 rlang_1.0.4
[79] pkgconfig_2.0.3 matrixStats_0.62.0 distributional_0.3.0
[82] evaluate_0.16 lattice_0.20-45 purrr_0.3.4
[85] labeling_0.4.2 rstantools_2.2.0 htmlwidgets_1.5.4
[88] processx_3.7.0 tidyselect_1.1.2 plyr_1.8.7
[91] magrittr_2.0.3 R6_2.5.1 generics_0.1.3
[94] DBI_1.1.3 pillar_1.8.1 withr_2.5.0
[97] xts_0.12.1 abind_1.4-5 survival_3.4-0
[100] tibble_3.1.8 crayon_1.5.1 utf8_1.2.2
[103] tzdb_0.3.0 rmarkdown_2.16 grid_4.2.1
[106] callr_3.7.2 git2r_0.30.1 forcats_0.5.2
[109] threejs_0.3.3 webshot_0.5.3 digest_0.6.29
[112] xtable_1.8-4 httpuv_1.6.5 RcppParallel_5.1.5
[115] stats4_4.2.1 munsell_0.5.0 viridisLite_0.4.1
[118] bslib_0.4.0 shinyjs_2.1.0